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Fusion of multiple diverse predictors in stock market · Study

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ArticleFusion of multiple diverse predictors in stock market

Sasan Barak2016

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Articlegreen2016

Fusion of multiple diverse predictors in stock market

Sasan Barak · Azadeh Arjmand · Sergio Ortobelli Lozza

90 citations·0/0 files stored·Information Fusion

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Forecasting Techniques and Applications

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2013Forecasting Stock Returns 2005The use of data mining and neural networks for forecasting stock market returns 2010Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches 2014Neural network ensemble operators for time series forecasting 2011A hybrid stock selection model using genetic algorithms and support vector regression 2006Stock market prediction with multiple classifiers

Cited by

2021Stock Trend Prediction Using Candlestick Charting and Ensemble Machine Learning Techniques With a Novelty Feature Engineering Scheme 2020Predicting next day direction of stock price movement using machine learning methods with persistent homology: Evidence from Kuala Lumpur Stock Exchange 2020Predicting Stock Price Using Two-Stage Machine Learning Techniques 2022A developed stock price forecasting model using support vector machine combined with metaheuristic algorithms

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Year2016

Typearticle

Published2016-11-09

VenueInformation Fusion

ISSN-L1566-2535

Languageen

Citations90

Files0 public / 0

ShelfStock Market Forecasting Methods

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doi10.1016/j.inffus.2016.11.006

openalex_workW2554780437

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2022Evaluating the performance of ensemble classifiers in stock returns prediction using effective features

2022Decision Fusion for Stock Market Prediction: A Systematic Review

2024ADVANCED PREDICTIVE ANALYTICS FOR COMPREHENSIVE RISK ASSESSMENT IN FINANCIAL MARKETS: STRATEGIC APPLICATIONS AND SECTOR-WIDE IMPLICATIONS